Because tilt’s software is still in development, we can’t show its workflow from end to end. But tilt’s software is very similar to r2dii’s software, so we’ll show it instead.
Both tilt and r2dii workflows share common steps:
Private data from the bank.
Data from 2DII.
Bank data matched to 2DII data.
Data manually validated and prioritized by the bank.
Metric calculated using 2DII’s methodology.
Plot showing the technology mix.
---
title: "Dashboard with parameters"
output:
flexdashboard::flex_dashboard:
source_code: embed
params:
loanbook: "~/Downloads/data/from-bank/loanbook.csv"
abcd: "~/Downloads/data/from-2dii/abcd.csv"
scenario: "~/Downloads/data/from-2dii/scenario.csv"
regions: "~/Downloads/data/from-2dii/regions.csv"
to_validate: "~/Downloads/data/validation/to_validate.csv"
validated: "~/Downloads/data/validation/validated.csv"
sector: "oil and gas"
---
```{r setup, include=FALSE}
library(flexdashboard)
options(readr.show_col_types = FALSE)
show_code <- FALSE
```
# Page 1
Column {data-width=300}
-----------------------------------------------------------------------
### r2dii workflow
Because tilt's software is still in development, we can't show its workflow from
end to end. But tilt's software is very similar to [r2dii's
software](https://pacta.rmi.org/pacta-for-banks-2020/), so we'll show it
instead.
Both tilt and r2dii workflows share common steps:
1. Input bank data.
2 Input 2DII data.
3. Match bank and 2DII data.
4. Validate matched companies.
5. Calculate a metric using 2DII's methodology.
6. Visualize the metric.
```{r echo=show_code}
# r2dii software
# * Toy data from banks and from tilt data-management. Eventually tiltToyData.
library(r2dii.data)
# * Like tilt.company.match, eventually tiltMatch
library(r2dii.match)
# * Like tiltIndicator
library(r2dii.analysis)
# * Like tiltPlot
library(r2dii.plot)
# General software
library(tidyverse, warn.conflicts = FALSE)
library(DT)
```
Column {data-width=700 .tabset}
-----------------------------------------------------------------------
### 1. Bank data
```{r echo=show_code}
loanbook <- read_csv(params$loanbook)
```
```{r}
datatable(loanbook)
```
> Private data from the bank.
### 2. 2DII data
```{r echo=show_code}
abcd <- read_csv(params$abcd)
```
```{r}
datatable(abcd)
```
> Data from 2DII.
### 3. Matched data
```{r echo=show_code}
matched <- match_name(loanbook, abcd)
```
```{r}
datatable(matched)
```
> Bank data matched to 2DII data.
### 3. Validated data
```{r echo=show_code}
# The bank saves the matched data to validate it manually.
write_csv(matched, params$to_validate)
```
```{r echo=show_code}
validated <- read_csv(params$validated)
```
```{r echo=show_code}
# Where the number of perfect matches is greater than one, pick the one with
# highest priority
validated_and_prioritized <- prioritize(validated)
```
```{r}
datatable(validated_and_prioritized)
```
> Data manually validated and prioritized by the bank.
### 4. Calculated metric
```{r echo=show_code}
# Datasets that the bank gets from a data provider
scenario <- read_csv(params$scenario)
regions <- read_csv(params$regions)
metric <- validated_and_prioritized |>
target_market_share(abcd = abcd, scenario = scenario, region_isos = regions)
```
```{r}
datatable(metric)
```
> Metric calculated using 2DII's methodology.
### 5. Visualization
```{r echo=show_code}
# Pick the targets you want to plot
picked <- filter(
metric,
scenario_source == "demo_2020",
sector == params$sector,
region == "global",
metric %in% c("projected", "corporate_economy", "target_sds")
)
```
```{r}
# Plot the technology mix
qplot_techmix(picked)
```
> Plot showing the technology mix.